The response patterns of microbial functional genes involved in biogeochemical cycles to cadaver decay is a central topic of recent environmental sciences. However, the response mechanisms and pathways of the functional genes associated with the carbon (C) and nitrogen (N) cycling to cadaveric substances such as cadaverine and putrescine remain unclear. This study explored the variation of functional genes associated with C fixation, C degradation and N cycling and their influencing factors under cadaverine, putrescine and mixed treatments. Our results showed only putrescine significantly increased the alpha diversity of C fixation genes, while reducing the alpha diversity of N cycling genes in sediment. For the C cycling, the mixed treatment significantly decreased the total abundance of reductive acetyl-CoA pathway genes (i.e., acsB and acsE) and lig gene linked to lignin degradation in water, while only significantly increasing the hydroxypropionate-hydroxybutylate cycle (i.e., accA) gene abundance in sediment. For the N cycling, mixed treatment significantly decreased the abundance of the nitrification (i.e., amoB), denitrification (i.e., nirS3) genes in water and the assimilation pathway gene (i.e., gdhA) in sediment. Environmental factors (i.e., total carbon and total nitrogen) were all negatively associated with the genes of C and N cycling. Therefore, cadaverine and putrescine exposure may inhibit the pathway in C fixation and N cycling, while promoting C degradation. These findings can offer some new insight for the management of amine pollution caused by animal cadavers.
As an inland dryland lake basin, the rivers and lakes within the Lake Bosten basin provide scarce but valuable water resources for a fragile environment and play a vital role in the development and sustainability of the local societies. Based on the Google Earth Engine (GEE) platform, combined with the geographic information system (GIS) and remote sensing (RS) technology, we used the index WI2019 to extract and analyze the water body area changes of the Bosten Lake basin from 2000 to 2021 when the threshold value is -0.25 and the slope mask is 8°. The driving factors of water body area changes were also analyzed using the partial least squares-structural equation model (PLS-SEM). The result shows that in the last 20 years, the area of water bodies in the Bosten Lake basin generally fluctuated during the dry, wet, and permanent seasons, with a decreasing trend from 2000 to 2015 and an increasing trend between 2015 and 2019 followed by a steadily decreasing trend afterward. The main driver of the change in wet season water bodies in the Bosten Lake basin is the climatic factors, with anthropogenic factors having a greater influence on the water body area of dry season and permanent season than that of wet season. Our study achieved an accurate and convenient extraction of water body area and drivers, providing up-to-date information to fully understand the spatial and temporal variation of surface water body area and its drivers in the basin, which can be used to effectively manage water resources.
The seasonal variation of petroleum pollution including n-alkanes in surface sediments of the Selangor River in Malaysia during all four climatic seasons was investigated using GC-MS. The concentrations of n-alkanes in the sediment samples did not significantly correlate with TOC (r = 0.34, p > 0.05). The concentrations of the 29 n-alkanes in the Selangor River ranged from 967 to 3711 µg g-1 dw, with higher concentrations detected during the dry season. The overall mean per cent of grain-sized particles in the Selangor River was 85.9 ± 2.85% sand, 13.5 ± 2.8% clay, and 0.59 ± 0.34% gravel, respectively. n-alkanes are derived from a variety of sources, including fresh oil, terrestrial plants, and heavy/degraded oil in estuaries. The results of this study highlight concerns and serve as a warning that hydrocarbon contamination is affecting human health. As a result, constant monitoring and assessment of aliphatic hydrocarbons in coastal and riverine environments are needed.
Urban agglomerations are sophisticated territorial systems at the mature stage of city development that are concentrated areas of production and economic activity. Therefore, the study of vulnerability from the perspective of production-living-ecological space is crucial for the sustainable development of the Yellow River Basin and global urban agglomerations. The relationship between productivity, living conditions, and ecological spatial quality is fully considered in this research. By constructing a vulnerability evaluation index system based on the perspectives of production, ecology, and living space, and adopting the entropy value method, comprehensive vulnerability index model, and obstacle factor diagnostic model, the study comprehensively assesses the vulnerability of the urban agglomerations along the Yellow River from 2001 to 2020. The results reveal that the spatial differentiation characteristics of urban agglomeration vulnerability are significant. A clear three-level gradient distribution of high, medium, and low degrees is seen in the overall vulnerability; these correspond to the lower, middle, and upper reaches of the Yellow River Basin, respectively. The percentage of cities with higher and moderate levels of vulnerability did not vary from 2001 to 2020, while the percentage of cities with high levels of vulnerability did. The four dimensions of economic development, leisure and tourism, resource availability, and ecological pressure are the primary determinants of the urban agglomeration's vulnerability along the Yellow River. And the vulnerability factors of various urban agglomerations showed a significant evolutionary trend; the obstacle degree values have declined, and the importance of tourism and leisure functions has gradually increased. Based on the above conclusions, we propose several suggestions to enhance the quality of urban development along the Yellow River urban agglomeration. Including formulating a three-level development strategy, paying attention to ecological and environmental protection, developing domestic and foreign trade, and properly planning and managing the tourism industry.
Saltwater intrusion in the coastal areas of Bangladesh is a prevalent phenomenon. However, it is not conducive to activities such as irrigation, navigation, fish spawning and shelter, and industrial usage. The present study analyzed 45 water samples collected from 15 locations in coastal areas during three seasons: monsoon, pre-monsoon, and post-monsoon. The aim was to comprehend the seasonal variation in physicochemical parameters, including water temperature, pH, electrical conductivity (EC), salinity, total dissolved solids (TDS), hardness, and concentrations of Na+, K+, Mg2+, Ca2+, Fe2+, HCO3-, PO43-, SO42-, and Cl-. Additionally, parameters essential for agriculture, such as soluble sodium percentage (SSP), sodium absorption ratio (SAR), magnesium absorption ratio (MAR), residual sodium carbonate (RSC), Kelly's ratio (KR), and permeability index (PI), were examined. Their respective values were found to be 63%, 16.83 mg/L, 34.92 mg/L, 145.44 mg/L, 1.28 mg/L, and 89.29%. The integrated water quality index was determined using entropy theory and principal component analysis (PCA). The resulting entropy water quality index (EWQI) and SAR of 49.56% and 63%, respectively, indicated that the samples are suitable for drinking but unsuitable for irrigation. These findings can assist policymakers in implementing the Bangladesh Deltaplan-2100, focusing on sustainable land management, fish cultivation, agricultural production, environmental preservation, water resource management, and environmental protection in the deltaic areas of Bangladesh. This research contributes to a deeper understanding of seasonal variations in the hydrochemistry and water quality of coastal rivers, aiding in the comprehension of salinity intrusion origins, mechanisms, and causes.
This study analyzed the spatial-temporal change pattern and underlying factors in production-living-ecological space (PLES) of Nanchong City, China, over the past 20 years using historical land use data (2000, 2010, 2020). A land use transfer matrix was calculated from the historical land use maps, and spatial analysis was conducted to analyze changes in the land use dynamics degree, standard deviation ellipse, and center of gravity. The results showed that there was a rapid spatial evolution of the PLES in Nanchong from 2000 to 2010, followed by a stabilization in the second decade. The transfer of ecological-production space occurred mainly in the Jialing and Yilong River basins, while the reduction of production space and the increase of living space were most prominent in the intersection of three districts (Shunqing, Jialing, and Gaoping districts). The return of production-ecological space was observed in the south and northeast of Yingshan, and there was little notable transfer of other types. The distribution of production space in Nanchong evolved in a north-south to east-west trend, with the center of gravity moving from Yilong to Peng'an County. The living space and production space expanded in a north-south direction, and the center of gravity position was in Nanbu, indicating a more balanced growth or decrease in the last 20 years. The changes in the spatial-temporal pattern of PLES in Nanchong were attributed to the intertwined factors of national policies, economic development, population growth, and the natural environment. This study introduced a novel approach towards rational planning of land resources in Nanchong, which may facilitate more sustainable urban planning and development.
The occurrence of floods has the potential to escalate the transmission of infectious diseases. To enhance our comprehension of the health impacts of flooding and facilitate effective planning for mitigation strategies, it is necessary to explore the flood risk management. The variability present in hydrological records is an important and neglecting non-stationary patterns in flood data can lead to significant biases in estimating flood quantiles. Consequently, adopting a non-stationary flood frequency analysis appears to be a suitable approach to challenge the assumption of independent and identically distributed observations in the sample. This research employed the generalized extreme value (GEV) distribution to examine annual maximum flood series. To estimate non-stationary models in the flood data, several statistical tests, including the TL-moment method was utilized on the data from ten stream-flow stations in Johor, Malaysia, which revealed that two stations, namely Kahang and Lenggor, exhibited non-stationary behaviour in their annual maximum streamflow. Two non-stationary models efficiently described the data series from these two specific stations, the control of which could reduce outbreak of infectious diseases when used for controlling the development measures of the hydraulic structures. Thus, the application of these models may help prevent biased prediction of flood occurrences leading to lower number of cases infected by disease.
The main aim of this study was to assess the presence of microplastics in the water and sediments of the Surakarta city river basin in Indonesia. In order to accurately reflect the river basin, a deliberate selection process was employed to choose three separate sampling locations and twelve sampling points. The results of the study revealed that fragments and fibers were the primary types of microplastics seen in both water and sediment samples. Furthermore, a considerable percentage of microplastics, comprising 53.8 % of the total, had dimensions below 1 mm. Moreover, the prevailing hues identified in the water samples were blue and black, comprising 45.1 % of the overall composition. In contrast, same color categories accounted for 23.3 % of the microplastics found in the soil samples. The analysis of microplastic polymers was carried out utilizing ATR-FTIR spectroscopy, which yielded the identification of various types including polystyrene, silicone polymer, polyester, and polyamide.
This study aimed to address the pressing issue of plastic pollution in aquatic ecosystems by assessing the prevalence and distribution of microplastics (MPs) in water and riverbank sediments of the Pekalongan River, a vital water source in Indonesia. From the present findings, MP concentrations in water ranged from 45.2 to 99.1 particles/L, while sediment concentrations ranged from 0.77 to 1.01 particles/g. This study revealed that fragment and film MPs constituted 30.1 % and 25.4 % of the total, respectively, with MPs measuring <1 mm and constituting 51.4 % of the total. Colored MPs, particularly blue and black MPs, accounted for 34 % of the total. The primary polymer components, as determined via Fourier transform infrared spectroscopy, were identified as polystyrene, polyester, and polyamide. In response to the escalating plastic waste crisis caused by single-use plastics, Pekalongan's local government implemented refuse segregation and recycling programs as part of its efforts to transition toward zero-waste practices.
This paper aims to select the most appropriate rain-based meteorological drought index for detecting drought characteristics and identifying tropical drought events in the Johor River Basin (JRB). Based on a multi-step approach, the study evaluated seven drought indices, including the Rainfall Anomaly Index (RAI), Standardized Precipitation Index (SPI), China-Z Index (CZI), Modified China-Z Index (MCZI), Percent of Normal (PN), Deciles Index (DI), and Z-Score Index (ZSI), based on the CHIRPS rainfall gridded-based datasets from 1981 to 2020. Results showed that CZI, MCZI, SPI, and ZSI outperformed the other indices based on their correlation and linearity (R2 = 0.96-0.99) along with their ranking based on the Compromise Programming Index (CPI). The historical drought evaluation revealed that MCZI, SPI, and ZSI performed similarly in detecting drought events, but SPI was more effective in detecting spatial coverage and the occurrence of 'very dry' and 'extremely dry' drought events. Based on SPI, the study found that the downstream area, north-easternmost area, and eastern boundary of the basin were more prone to higher frequency and longer duration droughts. Furthermore, the study found that prolonged droughts are characterized by episodic drought events, which occur with one to three months of 'relief period' before another drought event occurs. The study revealed that most drought events that coincide with El Niño, positive Indian Ocean Dipole (IOD), and negative Madden-Julian Oscillation (MJO) events, or a combination of these events, may worsen drought conditions. The application of CHIRPS datasets enables better spatiotemporal mapping and prediction of drought for JRB, and the output is pertinent for improving water management strategies and adaptation measures. Understanding spatiotemporal drought conditions is crucial to ensuring sustainable development and policies through better regulation of human activities. The framework of this research can be applied to other river basins in Malaysia and other parts of Southeast Asia.
A wastewater treatment plant (WWTP) is an essential part of the urban water cycle, which reduces concentration of pollutants in the river. For monitoring and control of WWTPs, researchers develop different models and systems. This study introduces a new deep learning model for predicting effluent quality parameters (EQPs) of a WWTP. A method that couples a convolutional neural network (CNN) with a novel version of radial basis function neural network (RBFNN) is proposed to simultaneously predict and estimate uncertainty of data. The multi-kernel RBFNN (MKRBFNN) uses two activation functions to improve the efficiency of the RBFNN model. The salp swarm algorithm is utilized to set the MKRBFNN and CNN parameters. The main advantage of the CNN-MKRBFNN-salp swarm algorithm (SSA) is to automatically extract features from data points. In this study, influent parameters (if) are used as inputs. Biological oxygen demand (BODif), chemical oxygen demand (CODif), total suspended solids (TSSif), volatile suspended solids (VSSif), and sediment (SEDef) are used to predict EQPs, including CODef, BODef, and TSSef. At the testing level, the Nash-Sutcliffe efficiencies of CNN-MKRBFNN-SSA are 0.98, 0.97, and 0.98 for predicting CODef, BODef, and TSSef. Results indicate that the CNN-MKRBFNN-SSA is a robust model for simulating complex phenomena.
A comprehensive geochemical study was conducted in the Sibuti River estuary by considering water, suspended solids (SS), and sediment samples from 36 stations during southwest monsoon (SWM) and northeast monsoon (NEM). In this study, the distribution of in situ parameters, major ions, nutrients, trace metals, and isotopes (δD, δ18O) were analyzed in water samples, whereas sediments and SS were studied for trace metals. The distribution revealed that suspended solids were the major carrier of Cd, Zn, and Mn, whereas sediments worked as a major source of Co, Cr, Ba, Se, Cu, and Pb. Na-Cl water type and ion exchange dominated the lower part of the estuary during both seasons. However, the mixed mechanism of Ca-Cl, Ca-Mg-Cl, and higher weathering indicated reverse ion exchange in the intermediate and upper parts of the estuary. Isotopic signatures of δD and δ18O in estuarine water indicate that the precipitation over the Limbang area dominates during SWM, whereas higher evaporation was confirmed during NEM. The factor analysis revealed that seawater influence in the estuary majority controlled the water chemistry irrespective of seasons. Major ions were mainly regulated by the tidal influence during the low flow time of the river (SWM), whereas the mixing mechanism of weathering and seawater controlled the concentrations during NEM. Nutrients such as NO3, SO42-, NH3, and NH4+ mainly originated from the agricultural fields and nitrification along with ammonification were responsible for the recycling of such nutrients. Trace metals except Cd were found to be geogenic in nature and originating mainly from the oxidation of pyrites present in the sandstone and mudstones of the Sibuti Formation. Redox condition was catalyzed by microorganisms near the river mouth, whereas Al-oxyhydroxides and Fe-oxyhydroxides complexes in the intermediate and upper part under oxygenated conditions controlled the absorption of metals. Overall, the estuary was found to be absorptive in nature due to ideal pH conditions and was confirmed by the saturation index (SI) of minerals.
The study explores the spatio-temporal variation of water quality parameters in the Hooghly estuary, which is considered an ecologically-stressed shallow estuary and a major distributary for the Ganges River. The estimated parameters are chlorophyll-a, total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM). The Sentinel-3 OLCI remote sensing imageries were analyzed for the duration of October 2018 to February 2019. We observed that the water quality of the Hooghly estuaries is comparatively low-oxygenated, mesotrophic, and phosphate-limited. Ongoing channel dredging for maintaining shipping channel depth keeps the TSM in the estuary at an elevated level, with the highest amount of TSM observed during March of 2019 (41.59g m-3) at station A, upstream point. Since the pre-monsoon season, TSM data shows a decreasing trend towards the mouth of the estuary. Chl-a concentration is higher during pre-monsoon than monsoon and post-monsoon periods, with the highest value observed in April at 1.09 mg m-3 in station D during the pre-monsoon period. The CDOM concentration was high in the middle section (January-February) and gradually decreased towards the estuary's head and mouth. The highest CDOM was found in February at locations C and D during the pre-monsoon period. Every station shows a significant correlation among CDOM, TSM, and Chl-a measured parameters. Based on our satellite data analysis, it is recommended that SNAP C2RCC be regionally used for TSM, Chl-a, and CDOM for water quality product retrieval and in various algorithms for the Hooghly estuary monitoring.
The concentration, distribution, and risk assessment of parabens were determined in the surface water of the Terengganu River, Malaysia. Target chemicals were extracted via solid-phase extraction, followed by high-performance liquid chromatography analysis. Method optimization produced a high percentage recovery for methylparaben (MeP, 84.69 %), ethylparaben (EtP, 76.60 %), and propylparaben (PrP, 76.33 %). Results showed that higher concentrations were observed for MeP (3.60 μg/L) as compared with EtP (1.21 μg/L) and PrP (1.00 μg/L). Parabens are ubiquitously present in all sampling stations, with >99 % of detection. Salinity and conductivity were the major factors influencing the level of parabens in the surface water. Overall, we found no potential risk of parabens in the Terengganu River ecosystem due to low calculated risk assessment values (risk quotient
Short-term streamflow prediction is essential for managing flood early warning and water resources systems. Although numerical models are widely used for this purpose, they require various types of data and experience to operate the model and often tedious calibration processes. Under the digital revolution, the application of data-driven approaches to predict streamflow has increased in recent decades. In this work, multiple linear regression (MLR) and random forest (RF) models with three different input combinations are developed and assessed for multi-step ahead short-term streamflow predictions, using 14 years of hydrological datasets from the Kulim River catchment, Malaysia. Introducing more precedent streamflow events as predictor improves the performance of these data-driven models, especially in predicting peak streamflow during the high-flow event. The RF model (Nash-Sutcliffe efficiency (NSE): 0.599-0.962) outperforms the MLR model (NSE: 0.584-0.963) in terms of overall prediction accuracy. However, with the increasing lead-time length, the models' overall prediction accuracy on the arrival time and magnitude of peak streamflow decrease. These findings demonstrate the potential of decision tree-based models, such as RF, for short-term streamflow prediction and offer insights into enhancing the accuracy of these data-driven models.
Mermithids are the most common parasites of black flies and are associated with host feminization and sterilization in infected hosts. However, information on the species / lineage of black fly mermithids in Southeast Asia, including Malaysia requires further elucidation. In this study, mermithids were obtained from black fly larvae collected from 138 freshwater stream sites across East and West Malaysia. A molecular approach based on nuclear-encoded 18S ribosomal RNA (18S rRNA) gene was used to identify the species identity / lineage of 77 nematodes successfully extracted and sequenced from the specimens collected. Maximum likelihood and neighbor-joining phylogenetic analyses demonstrated five distinct mermithid lineages. Four species delimitation analyses: automated simultaneous analysis phylogenetics (ASAP), maximum likelihood Poisson tree processes with Bayesian inferences (bPTP_ML), generalized mixed yule coalescent (GMYC) and single rate Poisson tree processes (PTP) were applied to delimit the species boundaries of mermithid lineages in this data set along with genetic distance analysis. Data analysis supports five distinct lineages or operational taxonomic units for mermithids in the present study, with two requiring further investigation as they may represent intraspecific variation or closely related taxa. One mermithid lineage was similar to that previously observed in Simulium nigrogilvum from Thailand. Co-infection with two mermithids of different lineages was observed in one larva of Simulium trangense. This study represents an important first step towards exploring other aspects of host - parasite interactions in black fly mermithids.
Contamination of water systems with endocrine disrupting chemicals (EDCs) is becoming a major public health concern due to their toxicity and ubiquity. The intrusion of EDCs into water sources and drinking water has been associated with various adverse health effects on humans. However, there is no comprehensive overview of the occurrence of EDCs in Malaysia's water systems. This report aims to describe the occurrence of EDCs and their locations. Literature search was conducted electronically in two databases (PubMed and Scopus). A total of 41 peer-reviewed articles published between January 2000 and May 2021 were selected. Most of the articles dealt with pharmaceuticals (16), followed by pesticides (7), hormones (7), mixed compounds (7), and plasticisers (4). Most studies (40/41) were conducted in Peninsular Malaysia, with 60.9% in the central region and almost half (48.8%) in the Selangor State. Only one study was conducted in the northern region and East Malaysia. The Langat River, the Klang River, and the Selangor River were among the most frequently studied EDC-contaminated surface waters, while the Pahang River and the Skudai River had the highest concentrations of some of the listed compounds. Most of the risk assessments resulted in a hazard quotient (HQ) and a risk quotient (RQ) 1 in the Selangor River. An RQ > 1 for combined pharmaceuticals was found in Putrajaya tap water. Overall, this work provides a comprehensive overview of the occurrence of EDCs in Malaysia's water systems. The findings from this review can be used to mitigate risks and strengthen legislation and policies for safer drinking water.
Global climate change has led to an increase in both the frequency and magnitude of extreme events around the world, the risk of which is especially imminent in tropical regions. Developing hydrological models with better capabilities to simulate streamflow, especially peak flow, is urgently needed to facilitate water resource planning and management as well as climate change mitigation efforts in the tropics. In view of the need, this paper explores the feasibility of improving streamflow simulation performance in the tropical Kelantan River Basin (KRB) of Peninsular Malaysia through coupling a conceptual process-based hydrological model - Soil and Water Assessment Tool (SWAT) with a deep learning model - Bidirectional Long Short-Term Memory (Bi-LSTM) in two ways. All SWAT parameters were set as their default values in one hybrid model (SWAT-D-LSTM), whereas three most sensitive SWAT parameters were calibrated in the other hybrid model (SWAT-T-LSTM). Comparison of daily streamflow simulation results have shown that SWAT-T-LSTM consistently performs better than SWAT-D-LSTM as well as the stand-alone SWAT and Bi-LSTM model throughout the simulation period. Particularly, SWAT-T-LSTM performs considerably better than the other three models in simulating daily peak flow. Based on the latest projection results of five GCMs from the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) under three emission scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), the best-performed SWAT-T-LSTM was run to assess the potential impacts of climate change on streamflow in the KRB. Ensemble assessment results have concluded that both average and extreme streamflow is much likely to increase considerably in the already wet northeast monsoon season from November to January, which has surely raised the alarm for more frequent flood occurrence in the KRB.
This study investigated physico-chemical interactions among Cu(II), biogenic materials, and Fe2O3 in a continuous-flow biofilm reactor system under a well-controlled environment. The effects of Fe2O3 and bacterial biofilms on the distribution of Cu(II) in a simulated aquatic environment were studied. To control biological and abiotic elements in the marine environment, a biofilm reactor was designed to understand the metal speciation of Cu(II) and its distribution. The reactor consisted of a biofilm chamber equipped with glass slides for biofilms attachment. Due to its ability to grow as biofilm in the medium, Pseudomonas atlantica was cultivated to adsorb trace Cu(II) to attached and suspended cells. It was found that biofilms with 170-285 mequiv chemical oxygen demand (COD) concentration/m2 of total oxidizable materials accelerated the Cu(II) adsorption to the surface of the reactor significantly by a factor of five. A significant inhibition to the bacterial growth took place (p ≤ 0.05; t-test) when Cu(II) concentration was higher than 0.5 mg/L. In the absence of Cu(II), bacterial cells grew normally to 0.075 of optical density (OD). However, at the Cu(II) concentration of 0.2 mg/L, the cells grew to a lower OD of 0.58. The presence of glycine and EDTA substantially reduced the toxicity of Cu(II) on bacterial growth (p ≤ 0.05; paired t-test). Their complexation with Cu(II) rendered the metal ions less available to bacterial cells. This implies that the Fe2O3 and bacterial biofilm affected Cu(II) distribution and speciation in the aquatic environment.
Heavy metals (HMs) are a vital elements for investigating the pollutant level of sediments and water bodies. The Murray-Darling river basin area located in Australia is experiencing severe damage to increased crop productivity, loss of soil fertility, and pollution levels within the vicinity of the river system. This basin is the most effective primary production area in Australia where agricultural productivity is increased the gross domastic product in the entire mainland. In this study, HMs contaminations are examined for eight study sites selected for the Murray-Darling river basin where the inverse Distance Weighting interpolation method is used to identify the distribution of HMs. To pursue this, four different pollution indices namely the Geo-accumulation index (Igeo), Contamination factor (CF), Pollution load index (PLI), single-factor pollution index (SPLI), and the heavy metal pollution index (HPI) are computed. Following this, the Pearson correlation matrix is used to identify the relationships among the two HM parameters. The results indicate that the conductivity and N (%) are relatively high in respect to using Igeo and PLI indexes for study sites 4, 6, and 7 with 2.93, 3.20, and 1.38, respectively. The average HPI is 216.9071 that also indicates higher level pollution in the Murray-Darling river basin and the highest HPI value is noted in sample site 1 (353.5817). The study also shows that the levels of Co, P, Conductivity, Al, and Mn are mostly affected by HMs and that these indices indicate the maximum HM pollution level in the Murray-Darling river basin. Finally, the results show that the high HM contamination level appears to influence human health and local environmental conditions.